README.md

metadata

pretty_name: CommunityBench
language:
  - en
task_categories:
  - text-classification
  - text-generation
  - question-answering
tags:
  - benchmark
  - community-alignment
  - reddit
  - preference-identification
  - distribution-prediction
  - communication-prediction
  - steering-generation
size_categories:
  - 10K<n<100K
license: unknown

CommunityBench

Dataset Description

CommunityBench is a benchmark dataset for evaluating language models' ability to understand and align with online community preferences. The dataset is constructed from Reddit posts and comments, focusing on real-world scenarios where models need to reason about community values, predict preference distributions, identify community-specific communication patterns, and generate content that aligns with community norms.

Dataset Structure

The dataset consists of two splits:

• train.jsonl: Training set
• test.jsonl: Test/evaluation set

Each line in the JSONL files contains a JSON object representing a single sample.

Task Types

The dataset includes four distinct tasks:

1. pref_id (Preference Identification): Identify which option best matches a community's preferences
2. dist_pred (Distribution Prediction): Predict the popularity distribution across multiple options
3. com_pred (Communication Prediction): Predict community-specific communication patterns
4. steer_gen (Steering Generation): Generate content that aligns with community norms and preferences

Dataset Statistics

• Task distribution: Each task type (pref_id, dist_pred, com_pred, steer_gen) has an equal number of samples
• Options per sample (for tasks with options): Average ~4.0 options per sample

Usage

You can load and use the dataset with the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset("jylin001206/communitybench", split="train")

Or load specific splits:

train_dataset = load_dataset("jylin001206/communitybench", split="train")
test_dataset = load_dataset("jylin001206/communitybench", split="test")

Data Fields

Each sample in the dataset contains community portraits, request-option sets, and task-specific annotations. The exact schema depends on the task type and includes information about:

• Subreddit and thread context
• Community portraits
• Request-option pairs
• Ground truth labels or distributions